code: def _is_qat_saved_model(saved_model_path: str):
      """Checks if the SavedModel is QAT-enabled by looking for 'FakeQuant' ops."""
      saved_model_proto = saved_model_loader.parse_saved_model(
          saved_model_path
      )
      for meta_graph in saved_model_proto.meta_graphs:
          if any(
              node.op.startswith("FakeQuant")
              for node in meta_graph.graph_def.node
          ):
              return True
          for function in meta_graph.graph_def.library.function:
              if any(
                  node.op.startswith("FakeQuant")
                  for node in function.node_def
              ):
                  return True
      return False
  
imports: import tempfile
  from typing import Mapping, Optional
  from absl import logging
  from tensorflow.compiler.mlir.quantization.tensorflow import quantization_options_pb2
  from tensorflow.compiler.mlir.quantization.tensorflow.python import py_function_lib
  from tensorflow.compiler.mlir.quantization.tensorflow.python import pywrap_quantize_model
  from tensorflow.compiler.mlir.quantization.tensorflow.python import representative_dataset
  from tensorflow.compiler.mlir.quantization.tensorflow.python import save_model
  from tensorflow.core.protobuf import meta_graph_pb2
  from tensorflow.python.lib.io import file_io
  from tensorflow.python.saved_model import load
  from tensorflow.python.saved_model import loader_impl
  from tensorflow.python.saved_model import signature_constants
  from tensorflow.python.saved_model import tag_constants
  from tensorflow.python.trackable import autotrackable
  from tensorflow.python.util import tf_export
label_desc: Use image node_id 1 for calling a global function with example usage: _is_qat_saved_model(saved_model_path) and returns: False, True, True